Expressive Computing Lab

표현적컴퓨팅 연구실

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표현적컴퓨팅 연구실

AI를 통한 인간능력의 증강, AI와의 협업 경험을 향상하기 위해 인간과 AI간의 상호작용 디자인은 더 많은 연구가 필요한 이 시대의 중요한 분야가 되고 있습니다. 그러나 AI 분야 연구분야가 가진 기술적 장벽으로 인해 그동안 디자인 관점은 충분히 고려되지 않았으며, 디자이너나 디자인과 관련된 연구를 하는 사람들의 참여도 많지 않았습니다. 이 두 가지 다른 세계를 연결하기 위해 저는 일상적인 맥락에서 사람들의 삶의 질을 개선하기 위한 새로운 인터랙션 디자인 자료, 도구 및 프로세스에 AI를 사용하는 것을 연구 주제로 삼고 있으며, 인간중심의 디자인방법을 인간-컴퓨터 상호작용 측면에서 (Human-Computer Interaction) 또는 인간-AI 협업(Human-AI Collaboration/Co-creation) 관점에서 사용자중심형, 디자인중심형 연구를 진행하고 있습니다. 예를 들어 우리 연구실에서는 지식 근로자의 생산성 고양 문제, 온라인 플랫폼과 콘텐츠의 중독성 있는 사용으로 인한 신체적, 정신적 건강 문제, 표현적인 제스처와 자세를 인식한 창의적인 이용 및 인터페이스 디자인 등 사람들이 일상에서 경험하는 작지만 중요한 다양한 문제를 AI를 통해 해결하고 있습니다.
The design of human–AI interaction is becoming an important area that requires further research to improve communication between people and AI systems. However, a design perspective has not been given due consideration because of the technical barriers inherent to the field of AI. To bridge these two different worlds, my research theme concerns the use of AI for new interaction design materials, tools, and processes to improve people’s quality of life in everyday contexts, with a strong focus on adding human-centered design considerations into the loop from a human-computer interaction as well as human-ai co-creation perspective. For example, our lab is tackling various small yet important challenges that people experience in everyday life, such as productivity among knowledge workers, physical and mental health issues caused by the addictive use of online platforms and content, and the use of expressive gestures and postures to support the user’s creative activities.

Major research field

Human-computer interaction, Machine learning, Gesture recognition, Intelligent UI design, Digital media design

Desired field of research

Human-centered machine learning, Deep technical UI/UX design, Information design, Emerging media arts

Research Keywords and Topics

# human-centered machine learning
# deep-technical ui/ux
# emerging media arts for audio/visual expression
HCML combines human insights and domain expertise with data-driven predictions to build a better, specialized model that supports various design tasks. How might we design more intelligent interactions and rich experiences through ML? We examine cutting-edge machine learning approach from a human-centered perspective, including explicitly recognizing human works, as well as reframing machine learning workflows based on situated human working practices, and exploring the co-adaptation of humans and systems. A human-centered understanding of machine learning in the design context can lead not only to more usable services and products but to new ways of framing design problem computationally with a deep technical UX design approach.

Research Publications

∙ Seonuk Kim and Kyungho Lee*. Designing Interfaces for Text-to-Image Prompt Engineering Using Stable Diffusion Models: a Human-AI Interaction Approach. Proceedings of The International Association of Societies of Design Research (IASDR’23). Milano, Italy
∙ Kyungho Lee*. Designing an Intelligent Learning System For Practicing the Oboe Embouchure. Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers. 2022. (UBICOMP’22) Cambridge, UK
∙ Proceedings of the 25th International Symposium on Electronic Art (ISEA'19), Misplaced Euphoria: Developing Kinesthetic Empathy Through Interactive Performance, Kyungho Lee, 2019
∙ Proceedings of the 9th ACM SIGCHI Conference on Creativity and Cognition (C&C'15) Express it!: An Interactive System for
Visualizing Expressiveness of Conductor’s Gestures, Kyungho Lee, Donna J Cox and Guy Garnett, 2015


  • EE. 정보/통신
  • EE10. U-컴퓨팅
  • EE1003. U-컴퓨팅 기기/주변기기


  • 정보-지식-지능화 사회 구현
  • 011600. 디지털 정보디자인 기술


  • 녹색기술관련 과제 아님


  • IT 분야
  • 정보처리 시스템 및 S/W
  • 010316. 기타 정보처리시스템 및 S/W 기술